CN111091385B - Weight-based object identification method and device and electronic equipment - Google Patents
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Abstract
The application provides an object identification method and device based on weight and electronic equipment, relates to the technical field of data identification, and solves the technical problem of low accuracy of identification results of user hazard degrees. The method comprises the following steps: determining a plurality of user objects, and converting the relations among the plurality of user objects into a relation network diagram; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the nodes represent the user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of the user objects; dividing a plurality of nodes according to the weights and the labels to obtain a plurality of communities; judging a target community to which an object to be identified belongs in a plurality of communities; and identifying the risk data of the object to be identified according to the risk data of the target community.
Description
Technical Field
The present disclosure relates to the field of data recognition technologies, and in particular, to a weight-based object recognition method and apparatus, and an electronic device.
Background
Currently, in a scenario where a risk level of a certain user needs to be identified, the process of identifying the risk level needs to consider many aspects. For example, the level of fraud risk for the lending individual is calculated by means of the individual's historical lending performance, basic revenue expense, demographic information, and the like.
However, in the process of carrying out fraud risk degree cluster division on a plurality of users, the consideration angle is single, the actual situation cannot be closed, the user risk degree is wrongly identified, and the accuracy of the identification result of the user risk degree is low easily.
Disclosure of Invention
The invention aims to provide a weight-based object identification method and device and electronic equipment, so as to solve the technical problem of low accuracy of identification results of dangerous degrees of users.
In a first aspect, an embodiment of the present application provides a weight-based object identification method, where the method includes:
determining a plurality of user objects, and converting the relations among the plurality of user objects into a relation network diagram; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the nodes represent the user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of the user objects;
dividing a plurality of nodes according to the weights and the labels to obtain a plurality of communities;
judging a target community to which an object to be identified belongs in a plurality of communities;
and identifying the risk data of the object to be identified according to the risk data of the target community.
In one possible implementation, the step of dividing the plurality of nodes according to the weights to obtain a plurality of communities includes:
calculating the weight sum of connecting edges between all second nodes in communities to which the adjacent nodes of the first nodes belong and the first nodes aiming at each first node in the relation network graph;
and if the weight sum is larger than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong.
In one possible implementation, the adjacent node is a node in the relationship network graph, where a node distance between the adjacent node and the first node is smaller than a preset distance.
In one possible implementation, the step of dividing the plurality of nodes according to the weights to obtain a plurality of communities further includes:
in the weight sum larger than the first preset weight value, if the difference between the first weight sum and the second weight sum is smaller than the second preset weight value, determining that the first node is divided into a first community and a second community at the same time;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weight sum is the weight sum of the connecting edges between all nodes in the second community and the first node.
In one possible implementation, the step of dividing the plurality of nodes according to the weights to obtain a plurality of communities further includes:
in the weight sum larger than the first preset weight value, if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value, determining that the first node is divided into a third community and a fourth community at the same time;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; and the fourth weight sum is the weight sum of connecting edges between all nodes in the fourth community and the first node.
In one possible implementation, before the step of dividing the plurality of nodes according to the weights and the labels to obtain a plurality of communities, the method further includes:
and carrying out data standardization processing on the weight of each connecting edge in the relation network diagram.
In one possible implementation, the number of contacts includes any one or more of:
number of call communications, number of short message communications, and number of network interactions.
In a second aspect, there is provided a weight-based object recognition apparatus, comprising:
the determining module is used for determining a plurality of user objects and converting the relations among the plurality of user objects into a relation network diagram; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the nodes represent the user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of the user objects;
the dividing module is used for dividing the nodes according to the weights and the labels to obtain a plurality of communities;
the judging module is used for judging a target community to which the object to be identified belongs in the communities;
and the identification module is used for identifying the risk data of the object to be identified according to the risk data of the target community.
In a third aspect, an embodiment of the present application further provides an electronic device, including a memory, and a processor, where the memory stores a computer program that can be executed by the processor, and the processor executes the method according to the first aspect.
In a fourth aspect, embodiments of the present application further provide a computer-readable storage medium storing machine-executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of the first aspect described above.
The embodiment of the application brings the following beneficial effects:
according to the object identification method, device and electronic equipment based on the weight, the plurality of user objects can be determined, the relation among the plurality of user objects is converted into the relation network diagram comprising the plurality of nodes, wherein labels representing risk data of the user objects are marked on the nodes representing the user objects, the plurality of nodes correspond to different connecting edges representing the weight of the contact times among the plurality of user objects, then the plurality of nodes are divided according to the weight and the labels to obtain a plurality of communities, and then the target communities to which the objects to be identified belong are judged in the communities, so that the risk data of the objects to be identified are identified according to the risk data of the target communities.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present application, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flowchart illustration of a weight-based object recognition method provided by an embodiment of the present application;
FIG. 2 is another flow chart diagram illustration of a weight-based object recognition method provided by an embodiment of the present application;
fig. 3 is a schematic structural diagram of an object recognition device based on weight according to an embodiment of the present application;
fig. 4 is a schematic structural diagram illustrating an electronic device according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more apparent, the technical solutions of the present application will be clearly and completely described below with reference to the accompanying drawings, and it is apparent that the described embodiments are some, but not all, embodiments of the present application. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present disclosure.
The terms "comprising" and "having" and any variations thereof, as used in the embodiments of the present application, are intended to cover non-exclusive inclusion. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those listed but may optionally include other steps or elements not listed or inherent to such process, method, article, or apparatus.
Currently, in a real social network, if the relationship is not connected again, the relationship strength between two people is increased according to the increase of the connection times and is decreased with the decrease of time, and finally, the relationship link is disconnected and is reduced. Thus, frauds are only measured from a single individual perspective, and risk propagation between associated individuals cannot be used to identify potentially high-risk lending activities, and prior art techniques fail to effectively risk identify when data packaging borrowers are encountered.
Based on the above, the embodiment of the application provides a weight-based object identification method and device and electronic equipment. The method can solve the technical problem of low accuracy of the recognition result of the dangerous degree of the user.
Embodiments of the present invention are further described below with reference to the accompanying drawings.
Fig. 1 is a flow chart of an object recognition method based on weight according to an embodiment of the present application. As shown in fig. 1, the method includes:
s110, determining a plurality of user objects, and converting the relation among the plurality of user objects into a relation network diagram.
It should be noted that, the relationship network graph includes a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes.
The nodes represent user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of user objects. In this step, the relationship data between the plurality of user objects may be converted into relationship network graph data, and the weight of the initial connection edge may be determined according to the number of contacts C.
In practice, the association between two nodes may include various forms of communication, such as telephone communication, network interaction, etc.
And S120, dividing the plurality of nodes according to the weights and the labels to obtain a plurality of communities.
The community may be a community in a community discovery algorithm, similar to a cluster. As shown in fig. 1, the weight in this step is the weight used to indicate the number of contacts between the plurality of user objects in step S110. The label in this step is the label for representing the risk data of the user object in step S110.
S130, judging a target community to which the object to be identified belongs in the communities.
It should be noted that the object to be identified may be a user to be identified. The target community is a community in the relational network graph.
And S140, identifying risk data of the object to be identified according to the risk data of the target community.
In this embodiment of the present application, the weights of the connection edges are different, and the number of times of connection between the sample objects may affect the weights. For example, user a frequently contacts risk user B, and the risk user B applies for a package for a loan or other loan, and since risk user B is skilled, user a is not contacted during the package, and in this case, the relationship between user a and risk user B is ignored. The weight-based object recognition method provided by the embodiment of the application can be suitable for scenes sensitive to the contact times and intensity, such as a relation network formed by vehicles, danger personnel, drivers, insurance beneficiaries and the like in insurance anti-fraud.
The above steps are described in detail below.
In some embodiments, the step S120 may include the following steps:
for each first node in the relation network graph, calculating the weight sum of connecting edges between all second nodes in communities to which the adjacent nodes of the first node belong and the first node;
if the weight sum is larger than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong.
Illustratively, node C in the relational network graph is randomly selected, and all nodes and connecting edges with a step length of 1 from node C are obtained, which is equivalent to obtaining all neighbor nodes of node C and the links between them. The sum of the connecting edge weights between the node C and all the nodes in the community to which one of the neighbor nodes belongs determines whether to divide the node C into the communities to which the neighbor nodes belong.
Since all communities to which the neighbor nodes belong are associated with the node C, in the embodiment of the present application, it is determined which community a finally belongs to according to the sum SW of weights connected between them and the node C.
The weight sum of the connecting edges among all the nodes in the community is calculated, so that the nodes can be more comprehensively and comprehensively combined to all the nodes in the community, the node dividing process is more comprehensively considered, and the community dividing result is more reasonable and accurate.
In some embodiments, the neighboring node is a node in the relationship network graph having a node distance from the first node that is less than a preset distance.
The above-mentioned neighboring node may be defined as a node having a sufficiently small node distance from the first node. For example, the node distance from the first node is a node of one unit distance. By defining adjacent nodes, the node dividing process is finer and more accurate, so that the accuracy of the node dividing result is guaranteed.
In some embodiments, the step S120 may further include the following steps:
in the weight sum larger than the first preset weight value, if the difference between the first weight sum and the second weight sum is smaller than the second preset weight value, determining to divide the first node into the first community and the second community at the same time;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weight sum is the weight sum of the connecting edges between all the nodes in the second community and the first node.
In practical applications, a threshold delta (w 1) may also be set, and if SW1-SW2< delta (w 1), it is determined that node C belongs to both community 1 and community 2. SW1 is the sum of weights of connection edges between all nodes in the community 1 to which the adjacent node 1 belongs and the node C, and SW2 is the sum of weights of connection edges between all nodes in the community 2 to which the adjacent node 2 belongs and the node C.
In the embodiment of the application, the weight sum among the communities is compared, and the target node is simultaneously divided into the communities under the condition of small comparison phase difference, so that the situation that the target node is simultaneously affiliated to the communities can be considered, the node dividing process is combined more comprehensively, and the community dividing result is more accurate.
In some embodiments, the step S120 may further include the following steps:
in the weight sum larger than the first preset weight value, if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value, determining that the first node is divided into a third community and a fourth community at the same time;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; the fourth weight sum is the weight sum of the connecting edges between all nodes in the fourth community and the first node.
In this embodiment, another threshold delta (w 2) may be set, and if SW3-SW4< delta (w 2), it is determined that node C belongs to both community 3 and community 4. SW3 is the sum of weights of connection edges between all nodes in the community 3 to which the adjacent node 2 belongs and the node C, and SW4 is the sum of weights of connection edges between all nodes in the other community 4 to which the adjacent node 2 belongs and the node C.
Through the comparison of the weight sum among a plurality of communities, when the comparison phase difference is smaller, the nodes are simultaneously divided into the communities, the condition that the nodes are simultaneously affiliated to the plurality of nodes can be considered, the node division process is combined more comprehensively, and the community division result is more accurate.
In some embodiments, prior to step S120, the method may further comprise the steps of:
and carrying out data standardization processing on the weight of each connecting edge in the relational network graph.
The weight of all the connecting edges in the relation network graph can be defined by the uniform weight standard through data standardization processing, so that the weight value of each connecting edge is more accurate, and the accuracy of community division results is guaranteed.
In some embodiments, the number of contacts includes any one or more of the following: number of call communications, number of short message communications, and number of network interactions.
The contact manner in the embodiment of the application is not limited to any communication form, and can be any interaction manner of terminal call communication, terminal short message communication, terminal network communication and the like. Therefore, the weight value determined according to the contact times can be more in accordance with the actual situation among the sample objects.
Fig. 3 provides a schematic structural diagram of an object recognition apparatus based on weight. As shown in fig. 3, the weight-based object recognition apparatus 300 includes:
a determining module 301, configured to determine a plurality of user objects, and convert relationships between the plurality of user objects into a relationship network graph; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the node represents a user object, the label represents risk data of the user object, and the weight of the connecting edge is used for representing the contact times among a plurality of user objects;
the dividing module 302 is configured to divide the plurality of nodes according to the weights and the labels to obtain a plurality of communities;
a judging module 303, configured to judge, from among a plurality of communities, a target community to which an object to be identified belongs;
the identifying module 304 is configured to identify risk data of the object to be identified according to risk data of the target community.
In some embodiments, the partitioning module 302 is specifically configured to:
for each first node in the relation network graph, calculating the weight sum of connecting edges between all second nodes in communities to which the adjacent nodes of the first node belong and the first node;
if the weight sum is larger than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong.
In some embodiments, the neighboring node is a node in the relationship network graph having a node distance from the first node that is less than a preset distance.
In some embodiments, the partitioning module 302 is further to:
in the weight sum larger than the first preset weight value, if the difference between the first weight sum and the second weight sum is smaller than the second preset weight value, determining to divide the first node into the first community and the second community at the same time;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weight sum is the weight sum of the connecting edges between all the nodes in the second community and the first node.
In some embodiments, the partitioning module 302 is further to:
in the weight sum larger than the first preset weight value, if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value, determining that the first node is divided into a third community and a fourth community at the same time;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; the fourth weight sum is the weight sum of the connecting edges between all nodes in the fourth community and the first node.
In some embodiments, the apparatus further comprises:
and the processing module is used for carrying out data standardization processing on the weight of each connecting edge in the relation network graph.
In some embodiments, the number of contacts includes any one or more of the following:
number of call communications, number of short message communications, and number of network interactions.
The weight-based object recognition device provided by the embodiment of the application has the same technical characteristics as the weight-based object recognition method provided by the embodiment, so that the same technical problems can be solved, and the same technical effects can be achieved.
As shown in fig. 4, an electronic device 400 provided in an embodiment of the present application includes: a processor 401, a memory 402 and a bus, said memory 402 storing machine-readable instructions executable by said processor 401, said processor 401 communicating with said memory 402 via the bus when the electronic device is running, said processor 401 executing said machine-readable instructions to perform the steps of the weight based object recognition method as described above.
Specifically, the above-described memory 402 and the processor 401 can be general-purpose memories and processors, and are not particularly limited herein, and the above-described weight-based object recognition method can be performed when the processor 401 runs a computer program stored in the memory 402.
In response to the weight-based object recognition method described above, embodiments of the present application also provide a computer-readable storage medium storing machine-executable instructions that, when invoked and executed by a processor, cause the processor to perform the steps of the weight-based object recognition method described above.
Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.
Claims (7)
1. A weight-based object recognition method, the method comprising:
determining a plurality of user objects, and converting the relations among the plurality of user objects into a relation network diagram; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the nodes represent the user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of the user objects;
dividing a plurality of nodes according to the weights and the labels to obtain a plurality of communities;
judging a target community to which an object to be identified belongs in a plurality of communities;
identifying risk data of the object to be identified according to the risk data of the target community;
dividing the plurality of nodes according to the weights to obtain a plurality of communities, wherein the method comprises the steps of:
calculating the weight sum of connecting edges between all second nodes in communities to which the adjacent nodes of the first nodes belong and the first nodes aiming at each first node in the relation network graph;
if the weight sum is larger than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong;
dividing the plurality of nodes according to the weights to obtain a plurality of communities, and further comprising:
in the weight sum larger than the first preset weight value, if the difference between the first weight sum and the second weight sum is smaller than the second preset weight value, determining that the first node is divided into a first community and a second community at the same time;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weight sum is the weight sum of connecting edges between all nodes in the second community and the first node;
dividing the plurality of nodes according to the weights to obtain a plurality of communities, and further comprising:
in the weight sum larger than the first preset weight value, if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value, determining that the first node is divided into a third community and a fourth community at the same time;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; and the fourth weight sum is the weight sum of connecting edges between all nodes in the fourth community and the first node.
2. The method of claim 1, wherein the neighboring node is a node in the relationship network graph having a node distance from the first node that is less than a preset distance.
3. The method of claim 1, further comprising, prior to the step of partitioning the plurality of nodes into a plurality of communities based on the weights and the labels:
and carrying out data standardization processing on the weight of each connecting edge in the relation network diagram.
4. The method of claim 1, wherein the number of contacts comprises any one or more of:
number of call communications, number of short message communications, and number of network interactions.
5. A weight-based object recognition apparatus, comprising:
the determining module is used for determining a plurality of user objects and converting the relations among the plurality of user objects into a relation network diagram; the relation network graph comprises a plurality of nodes, labels are marked on the nodes, and connecting edges with different weights are corresponding to the nodes; the nodes represent the user objects, the labels represent risk data of the user objects, and the weights of the connecting edges are used for representing the contact times among a plurality of the user objects;
the dividing module is used for dividing the nodes according to the weights and the labels to obtain a plurality of communities;
the judging module is used for judging a target community to which the object to be identified belongs in the communities;
the identification module is used for identifying the risk data of the object to be identified according to the risk data of the target community;
the dividing module is specifically used for:
for each first node in the relation network graph, calculating the weight sum of connecting edges between all second nodes in communities to which the adjacent nodes of the first node belong and the first node;
if the weight sum is larger than a first preset weight value, determining to divide the first node into communities to which the adjacent nodes belong;
the partitioning module is further configured to:
in the weight sum larger than the first preset weight value, if the difference between the first weight sum and the second weight sum is smaller than the second preset weight value, determining to divide the first node into the first community and the second community at the same time;
the first community is a community to which a first adjacent node of the first node belongs, and the second community is a community to which a second adjacent node of the first node belongs;
the first weight sum is the weight sum of connecting edges between all nodes in the first community and the first node; the second weight sum is the weight sum of connecting edges between all nodes in the second community and the first node;
the partitioning module is further configured to:
in the weight sum larger than the first preset weight value, if the difference value between the third weight sum and the fourth weight sum is smaller than the third preset weight value, determining that the first node is divided into a third community and a fourth community at the same time;
the third community and the fourth community are two communities to which one adjacent node of the first node belongs at the same time;
the third weight sum is the weight sum of connecting edges between all nodes in the third community and the first node; the fourth weight sum is the weight sum of the connecting edges between all nodes in the fourth community and the first node.
6. An electronic device comprising a memory, a processor, the memory having stored therein a computer program executable on the processor, characterized in that the processor, when executing the computer program, implements the steps of the method of any of the preceding claims 1 to 4.
7. A computer readable storage medium storing machine executable instructions which, when invoked and executed by a processor, cause the processor to perform the method of any one of claims 1 to 4.
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| CN111860655B (en) * | 2020-07-23 | 2023-05-30 | 中国联合网络通信集团有限公司 | User processing method, device and equipment |
| CN112653669B (en) * | 2020-12-04 | 2022-08-12 | 智网安云(武汉)信息技术有限公司 | Network terminal security threat early warning method, system and network terminal management device |
| CN114997869A (en) * | 2021-02-26 | 2022-09-02 | 北京字节跳动网络技术有限公司 | Risk node identification method and device, electronic equipment and computer readable storage medium |
| CN116012169B (en) * | 2022-12-21 | 2024-03-22 | 南京睿聚科技发展有限公司 | Method and system for screening risk of insurance claim settlement based on position data |
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| CN104715034A (en) * | 2015-03-16 | 2015-06-17 | 北京航空航天大学 | Weighed graph overlapping community discovery method based on central persons |
| CN108648094A (en) * | 2018-05-08 | 2018-10-12 | 阿里巴巴集团控股有限公司 | A kind of community discovery method, device and equipment |
| CN109784636A (en) * | 2018-12-13 | 2019-05-21 | 中国平安财产保险股份有限公司 | Fraudulent user recognition methods, device, computer equipment and storage medium |
| CN109978710A (en) * | 2019-04-11 | 2019-07-05 | 重庆大学 | Overlapping community division method based on K- core iteration factor and community's degree of membership |
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| CN104166731A (en) * | 2014-08-29 | 2014-11-26 | 河海大学常州校区 | Discovering system for social network overlapped community and method thereof |
| CN104715034A (en) * | 2015-03-16 | 2015-06-17 | 北京航空航天大学 | Weighed graph overlapping community discovery method based on central persons |
| CN108648094A (en) * | 2018-05-08 | 2018-10-12 | 阿里巴巴集团控股有限公司 | A kind of community discovery method, device and equipment |
| CN109784636A (en) * | 2018-12-13 | 2019-05-21 | 中国平安财产保险股份有限公司 | Fraudulent user recognition methods, device, computer equipment and storage medium |
| CN109978710A (en) * | 2019-04-11 | 2019-07-05 | 重庆大学 | Overlapping community division method based on K- core iteration factor and community's degree of membership |
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